An integrative approach to predicting the functional effects of non-coding and coding sequence variation

By Hashem A. Shihab, Mark F. Rogers, Julian Gough, Matthew E. Mort, David N. Cooper, Ian N. M. Day, Tom R. Gaunt, Colin Campbell. Bioinform., 2015

631 citations recorded by the source corpus.

Motivation: Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict the functional consequences of both coding and non-coding sequence variants. Our method utilizes various genomic annotations, which have recently become available, and learns to weight the significance of each component annotation source. Results: We show that our method outperforms current state-of-the-art algorithms, CADD and GWAVA, when predicting the functional consequences of non-coding variants. In addition, FATHMM-MKL is comparable to the best of these algorithms when predicting the impact of coding variants. The method includes a confidence measure to rank order predictions. Availability and implementation: The FATHMM-MKL webserver is available at: http://fathmm.biocompute.org.uk Contact: H.Shihab@bristol.ac.uk or Mark.Rogers@bristol.ac.uk or C.Campbell@bristol.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b194759013375350b6f06d30b5a401ff1d12a4f5

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Citations in the corpus

LIST-S2: taxonomy based sorting of deleterious missense mutations across species (2020, methodology): “0a (17) (namely: SIFT (1), SIFT4G (18), PROVEAN (2), phyloP (3) X 3, SiPhy (5), phastCons (6) X 3, GERP++ (4), Eigen (10), CADD (9), DANN (11), PolyPhen-2 (8), FATHMM-MKL (19), PrimateAI (20), MutationTast (21), MutationAssessor (22), MPC (23) and fitCons (12) X 4) are used for benchmarking.”
dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Non-synonymous and Splice Site SNVs (2016, background): “2015), two conservation scores: 2× phastCons (Siepel et al. 2005), and allele frequencies from the UK10K cohorts (The UK10K Consortium 2015) and the Exome Aggregation Consortium (ExAC, http://exac.”
FATHMM-XF: accurate prediction of pathogenic point mutations via extended features (2017, neutral): “, Shihab, H. A., Mort, M., Cooper, D. N., Gaunt, T., & Campbell, C. (2018). FATHMM-XF: accurate prediction of pathogenic point mutations via extended features.”
The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies (2017, positive): “HGMD has also been used by a number of different groups to aid the development of a wide variety of post-NGS variant interpretation and exome prioritisation algorithms including MutPred (Li et al. 2009), MutPred Splice (Mort et al. 2014), PROVEAN (Choi et al. 2012), CAROL (Lopes et al. 2012), regSNP”
dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “On the other hand, some scores with similar algorithms do not have high agreement ratios: such as fathmm-XF and fathmm-MKL [53] (0.”
GAVIN: Gene-Aware Variant INterpretation for medical sequencing (2017, methodology): “In addition, we assessed the performance of GAVIN in compared to 12 common in silico tools for pathogenicity prediction: MSC (using two different settings), CADD (using three different thresholds), SIFT [5], PolyPhen2 [15], PROVEAN [16], Condel [17], PON-P2 [18], PredictSNP2 [19], FATHMMMKL [20], GW”